CONCLUSION

Predicting the fuel economy of the vehicle is an important component of automobile behaviour analytics.We have investigate the relationship between the dependent and independent variables.In our investigation for finding the best input variable we have used Correlation as statistical test and come to conclusion that Engine Displacement is the best predictor since both the scatter plot and pearson's correlation coefficient (r) of -0.79(approx) suggest a strong negative relationship between engine displacement and fuel economy.

Fuel Economy Analysis has a huge impact on Revenue of a company.

If the company predicts the correct and fruitful number of predictors along with correct fuel economy with high accuracy, it gives the company a estimate of how its revenues would look like and in turn give it freedom to plan finances ahead as well as give more information about what are the main concerns to look after.

Hence There are a number of other different insights that we could gain from the data, but this would be a good

initial list to investigate further if the company had even more detailed data sets.

These prediction from the discriminant model can help the business/company formulate strategies.Our final model got accuracy of 89% (approx).